A genetic programming-based hyper-heuristic approach for storage location assignment problem

Abstract

Abstract-This study proposes a method for solving real-world warehouse Storage Location Assignment Problem (SLAP) under grouping constraints by Genetic Programming (GP). Integer Linear Programming (ILP) formulation is used to define the problem. By the proposed GP method, a subset of the items is repeatedly selected and placed into the available current best location of the shelves in the warehouse, until all the items have been assigned with locations. A heuristic matching function is evolved by GP to guide the selection of the subsets of items. Our comparison between the proposed GP approach and the traditional ILP approach shows that GP can obtain near-optimal solutions on the training data within a short period of time. Moreover, the evolved heuristics can achieve good optimization results on unseen scenarios, comparable to that on the scenario used for training. This shows that the evolved heuristics have good reusability and can be directly applied for slightly different scenarios without any new search process.

Unit-load storage assignment strategy for warehouses in seismic areas

“…Research widely faces the storage assignment problem for both automatic and manual warehouses (Chan & Chan, 2011;Fontana & Cavalcante, 2014;Petersen, Charles, & Heiser, 2005;Van Den Berg & Zijm, 1999;Xie, Mei, Ernst, Li, & Song, 2014;Xu, Lim, Shen, Li, & IEEE., 2008). The formers are, typically, served by AS/RSs, jointly moving horizontally and vertically, allowing high handling service levels.…”

A restricted neighbourhood Tabu Search for Storage Location Assignment Problem

“…Many related works can be found for Job Shop Scheduling (JSS) [15] [16]. A Genetic Programming (GP)-based hyper-heuristic approach was proposed to search for the best allocation rules for a variant of the SLAP model in our previous study [9]. It includes the grouping constraints of the products.…”

“…However, one still prefers to put all of them together to facilitate management when possible. Taking this concern into account, grouping constraints for products are defined and a variant of SLAP called SLAP with Grouping Constraints (SLAP-GC) was proposed in [9]. In this paper, we focus on the investigation of SLAP-GC.…”

“…Despite the intensive study of SLAP, only the GP-based method [9] has been proposed for SLAP-GC. Furthermore, the problem has not been fully explored in larger instances, of which the size of the search space become prohibitive for branch-and-cut approach.…”

“…The resultant tabu search, called the Restricted Neighbourhood Tabu Search (RNTS), addresses the challenging feasibility issue by adopting the problem-specific search operators, which guarantee solution feasibility by restricting the neighbourhood during the search process. Experimental studies are carried out to compare the proposed RNTS with the branch-and-cut and the GP-based method [9] on the real-world test instances. The results demonstrate that RNTS outperforms both of the compared algorithms in terms of solution quality and running time for solving SLAP-GC.…”